Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Own the zero-to-one lifecycle for structured financial data products, from analysis and data architecture through launch, testing, and iterative enhancements. Translate investor research and quantitative workflows into product requirements, partner with engineering and platform teams, validate solutions with customers and domain experts, prioritize initiatives, analyze competitors, and drive cross-functional alignment. The role requires strong financial-data fluency, product leadership, communication, and attention to detail.

Lead Customer Success Operations to improve CS productivity and efficiency. Own metric definitions (NRR, GRR, renewal, usage KPIs), operationalize programs, support CS tooling and workflows (Salesforce, Gainsight, Clari, Gong, Tableau), build playbooks and reporting, resolve escalations, and partner cross-functionally to drive adoption, retention, and growth.

Provides second-line technical support for SaaS and private-cloud environments, diagnosing complex AWS, Kubernetes, API, search, and agentic-system issues. Partners with Engineering, Product, and customer-facing teams to resolve cases, lead technical account reviews, identify risks, improve processes, and create runbooks and documentation. Builds reusable AI workflows, prompt libraries, and MCP-connected investigation tools while mentoring junior engineers and supporting strategic customers.

Lead Customer Success Operations roadmap execution, scale processes and tools, own CS metrics strategy and reporting, manage a Senior Analyst, act as senior escalation for operational issues, partner cross-functionally (Product, Sales, Finance, BI), and operationalize scalable playbooks and governance for CS programs and QBRs.

Support Customer Success Operations by building and maintaining CS metrics and reporting (NRR, GRR, renewal, usage KPIs), partnering cross-functionally on complex deployments, and executing strategic CS programs. Serve as a go-to resource for account-level context, QA reporting, and initiatives like CS platform and usage-based pricing. Influence stakeholders, manage multiple priorities, and translate data into actionable insights for CS leadership.

Provide first-line customer and product support for AlphaSense’s platform via email, chat, and calls. Troubleshoot, triage, and escalate issues; drive documentation and training; partner with Product, Engineering, and SRE on incidents and feature feedback; own knowledge base updates and beta feature testing.

Lead quality strategy for an AI-first platform: define testing standards, metrics, and automation; validate AI models and data pipelines; design API and UI tests; coach teams on shift-left quality and release gates; improve observability and performance practices to ensure scalable, data-driven quality outcomes.

Own and evolve AlphaSense's structured financial data taxonomy, define label conventions and governance, collaborate cross-functionally to implement scalable, production-ready data structures, ensure AI/LLM compatibility, document standards, resolve onboarding inconsistencies, and build repeatable taxonomy frameworks for future growth.

Design, operate, and evolve CI/CD and delivery infrastructure for ~400 engineers. Build developer-facing tooling, champion progressive delivery (canaries, automated rollbacks), own end-to-end delivery from merge to production, improve pipeline reliability and speed, evolve the Internal Developer Portal, and embed AI-assisted workflows into the software development lifecycle.

Lead architecture and implementation to scale a large Kubernetes-native microservice platform into cell-based deployments. Decompose services, build shared libraries and configuration frameworks, enable private/isolated cloud deployments, land changes in product services, set architecture patterns, partner with Cloud Platform/SRE/DevEx, mentor senior engineers, and embed AI tooling into development workflows.